Advertising campaigns utilizing streaming analytics

ABSTRACT

The present invention provides methods and systems for use in advertising campaigns and advertisement targeting. Techniques are provided in which streams of data, including communications data, are sampled, such as during transmission to intended recipients. Sampled data may be analyzed and used to determine topics of interest. Sampled data may be analyzed or filtered to determine data suspected of being of particular significance or relevance in determining topics of interest. Determined topics of interest may be used in advertisement targeting as part of an advertising campaign.

BACKGROUND

Effectively spending advertising dollars, from the advertiser executiveperspective, is a challenging problem. Furthermore, even an otherwisesound advertising campaign can be rendered ineffective by incorrect,delayed, or late to market timing, and even very small delays can rendera campaign and its elements much less effective. This is particularlytrue as mobile devices, across the globe, are used to create andtransmit real-time data. Mobile users may create and transmit data at amaterially faster rate than existing advertising platforms caneffectively intake, absorb, and process in a timely, relevant andtargeted manner, from a campaign perspective.

There is a need for more effective techniques in advertising andadvertising campaigns.

SUMMARY

Some embodiments of the invention provide systems and methods for use inadvertising campaigns, including advertisement targeting. Techniques areprovided in which streams of data, including, for example,communications data, metadata, geo data, temporal data, etc., issampled, such as during transmission to intended recipients. Sampleddata may be analyzed and used to determine topics of interest. Sampleddata may be analyzed or filtered to determine data suspected of being ofparticular significance or relevance in determining topics of interest.Determined topics of interest may subsequently be used in advertisementtargeting as part of an advertising campaign.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a distributed computer system according to one embodiment ofthe invention;

FIG. 2 is a flow diagram illustrating a method according to oneembodiment of the invention;

FIG. 3 is a flow diagram illustrating a method according to oneembodiment of the invention;

FIG. 4 is a block diagram illustrating one embodiment of the invention;and

FIG. 5 is a block diagram illustrating one embodiment of the invention.

While the invention is described with reference to the above drawings,the drawings are intended to be illustrative, and the inventioncontemplates other embodiments within the spirit of the invention.

DETAILED DESCRIPTION

FIG. 1 is a distributed computer system 100 according to one embodimentof the invention. The system 100 includes or utilizes various computersand electronic devices. As depicted, the system includes user computers104, wireless devices 106, advertiser computers 108, and servercomputers 110.

Furthermore, the system can include or utilize any of numerous otherdistributed components. As depicted, these may include satellites orsatellite systems 112, clouds such as private clouds 114, cell towersand related systems 116, repeaters 118, intermediary service providers,and various forms of data 120.

Any of the various components of the system 100 may be coupled ornetworked together in various ways, which may include allowingbidirectional communication and data flow. Various networks and types ofnetworks may be included or utilized, which may include telephonenetworks, the Internet, wired and wireless networks, public and privatenetworks and clouds, etc. embodiments in which the Internet is notincluded, as well as embodiments in which other networks are included inaddition to the Internet, including one more wireless networks, WANs,LANs, telephone, cell phone, or other data networks, networks associatedwith satellites, repeaters, intermediary service providers, etc.Furthermore, embodiments are contemplated in which user computers orother devices may be or include wireless, portable, or handheld devicessuch as cell phones, smart phones, PDAs, tablets, etc.

Furthermore, while not depicted some embodiments of the inventioninclude one or more electronic data sampling devices or systems, as wellas other systems, devices, and components. Such devices or systems maybe separate or standalone, or may be integrated into other computers ordevices. Furthermore, such devices and systems may be coupled, such asthrough one or more networks, to other computers and devices, includingthe server computers, the advertiser computers, etc.

Each of the computers may be distributed, and can include varioushardware, software, applications, algorithms, programs and tools.Depicted computers may also include a hard drive, monitor, keyboard,pointing or selecting device, etc. The computers may operate using anoperating system such as Windows by Microsoft, etc. Each computer mayinclude a central processing unit (CPU), data storage device, andvarious amounts of memory including RAM and ROM. Depicted computers mayalso include various programming, applications, algorithms and softwareto enable searching, sampling, search results, and advertising, such asgraphical or banner advertising as well as keyword searching andadvertising in a sponsored search context. Many types of advertisementsare contemplated, including textual advertisements, rich advertisements,video advertisements, coupon-related advertisements, group-relatedadvertisements, social networking-related advertisements, network gamingads, virtual world ads, user-contributed content or video, etc.

As depicted, each of the server computers 110 includes one or more CPUs122 and a data storage device 124. The data storage device 124 includesa database 128 and Streaming Analytics Program 126.

As depicted, the server computer, 110, including the Streaming AnalyticsProgram 126, are coupled directly to various sources of data, which mayinclude streaming or real-time data, including, for example, perhapsamong others, satellites 112, cell towers 116, repeaters 118 and otherdata 120 or data sources, which may include other elements of the system100, or non-depicted or other elements. While the server computer 110 isdepicted as coupled to the data sources, in some embodiments, astreaming analytics program or streaming analytics system may otherwisebe directly so coupled.

In some embodiments, direct or otherwise fast, efficient connection orcoupling of the Streaming Analytics Program 126 to various data sourceseliminates, reduces, mitigates or collapses latency or ad-relatedlatency inherent in other arrangements. For example, ad latencyincreases when a data source first transmits data, which consequentlygoes through a network or networks, then arrives at an analytics systemor program. By contrast, according to some embodiments of the invention,direct connection of the Streaming Analytics Program 126 to data sourceseliminates or reduces such latency, allowing significant immediate anddownstream advantages in advertising, such as, for example, fasteridentification of relevant ad topics, faster ad selection, faster addelivery and campaign implementation, and, consequently, increasedtimeliness, relevance, or ad performance, increased campaign performanceand ROI, etc.

The Program 114 is intended to broadly include all programming,applications, algorithms, software and other and tools necessary toimplement or facilitate methods and systems according to embodiments ofthe invention. The elements of the Program 114 may exist on a singleserver computer or be distributed among multiple computers or devices.

FIG. 2 is a flow diagram illustrating a method 200 according to oneembodiment of the invention. At step 202, the method 200 includes using,one or more computers or electronic devices, sampling one or morestreams of electronic data, the electronic data including usercommunications data, to obtain sample data. Communications data orelectronic data can broadly include data such as voice data, messagingdata, email data, text data, blog data, content data, user generatedcontent, analog data, digital data, user-generated content data,metadata, geo data, temporal data, etc.

It is to be noted that, in some embodiments, multiple samplingtechniques or processes, or types of sampling techniques or processes,may be utilized, and may be performed serially, concurrently or in acompound manner (both serially and concurrently). Additionally, multipletesting or analytics techniques or processes may be run on sampled data,which also may be run serially, concurrently or in a compound manner.

At step 204, the method 200 includes, using one or more computers,analyzing the sample data to obtain targeting data for use in targetingelectronic advertisements to electronic device users, in which thetargeting data includes data relating to topics of interest to theelectronic device users. Some embodiments, it is noted, do not includeidentifying topics of interest.

At step 206, the method 200 includes, using one or more computers, basedat least in part on the targeting data, selecting electronicadvertisements for serving to targeted electronic device users.

At step 208, the method 200 includes, using one or more computers,serving the selected electronic advertisements to the targetedelectronic device users.

FIG. 3 is a flow diagram illustrating a method 300 according to oneembodiment of the invention. At step 302, the method 300 includes, usingone or more computers or electronic devices, sampling multiple streamsof electronic data, the electronic data including user communicationsdata, to obtain sample data. The sampling is conducted duringtransmission of the electronic data but before reception of theelectronic data by intended recipients. Sampling includes, but is notlimited to, collecting data from a plurality of data sources, such ascell phone transmission or reception structures, using electronicdevices at the cell phone transmission or reception structures,traditional connected transmission channels, etc. It is to be notedthat, in some embodiments, a plurality of sampling techniques can beapplied to the same data as the data is transmitted from the datasource.

Steps 304, 306 and 308 are similar to steps 204, 206 and 208 as depictedin FIG. 2

FIG. 4 is a block diagram 400 illustrating one embodiment of theinvention. Block 402 represents one or more data sources or datastreams. Data sources, among other things, cellular towers, voice anddata repeaters, cloud computing centers, publishers, content providers,various electronic communications, etc.

Block 404 represents data sampling, including use of one or more sensingor sampling systems. It is to be noted that, in some embodiments, one ormore sampling systems or sampling algorithms may be utilized, or acombination of sampling algorithms may be utilized. Furthermore, in someembodiments, a set or library of sampling algorithms may be utilized andone or more sampling algorithms may be selected therefrom.

At block 406, although the bulk of data is filtered and not sampled,some data is sampled. Furthermore, from the sampled data, suspect datais identified, which may be, for example, data suspected of being ordetermined to be of particular significance or relevance in usertargeting. For some simple examples, suspect data can be data thatmatches one or more keywords from a set, such as keywords that arelikely to relate to user interests, keywords associated with possibleadvertisement topics, etc. In some embodiments, all sample data may beutilized, and suspect data may not be identified among sample data.

At block 408, advertising campaign launch or integration is triggered,and, at block 410, sample or suspect data, or results of analysisthereof, is fed into an advertising campaign or advertisement targetingsystem(s). For example, these blocks can include, upon sufficientcollection, or collection and analysis, of sample or suspect data, usingsuch sample or suspect data, or the results of analysis of such data,triggering implementation of an ad campaign in which it will be used inad targeting, or to trigger integration into an ad campaign. In variousembodiments, sample or suspect data may be analyzed before triggering,feed in or integration into an ad campaign, or may be analyzed as partof an inventive component of such a campaign, for example. Someembodiments include integration with existing ad campaigns, includingtrafficking, analytics, billing, forecasting, targeting, ad selection,etc.

Block 412 represents near real-time or real-time use of sample orsuspect data, or results of analysis thereof, in an ad campaign, such asin ad targeting.

Block 420 represents one or more ad serving systems, which may be usedin serving ads and implementing ad campaigns.

Blocks 414, 416 and 418 represent prior art usage of data not includingtechniques according to embodiments of the invention. Specifically, atblock 414, data is passed to traditional applications and databases,such as database 416. At block 418, use of such traditional techniquesresults in delayed campaign usage or launch.

FIG. 5 is a block diagram 500 illustrating one embodiment of theinvention. Block 502 represents sampling, such as sampling of data froma data stream.

Block 506 represents analysis of sample data, which can includeidentification of suspect data. In embodiments, a feedback mechanism 504is utilized, in which sample analysis or suspect data identificationresults can be used to modify influence or guide future sampling, orsampling protocol or procedures. For example, once suspect data, or asufficient concentration or frequency of suspect data, etc., is found,the feedback mechanism, which may include models, algorithms, engines,etc., may call for, as an example, more frequent sampling for a periodthereafter, since it may be that more suspect data is likely to be foundduring such a period. Such analysis, identification and feedbackmechanism can include use of data from one or more databases or datastores, such as database 510, as well as one or more models oralgorithms 512, which can include predictive models, machine learningmodels, etc.

Block 510 represents one or more ad serving systems, which may beutilized in serving ads and implementing ad campaigns. This may includead hoc, dynamic or on the fly ad generation or selection, includingstreaming ads or media, based on fast or real-time data sampling andassociated analytics or algorithm output.

Some embodiments of the invention provide systems and methods thatfacilitate low to zero latency automated advertising campaigns usingreal-time data sensing, sampling, and detecting methods applied to databeing created or data in transit. Systems according to some embodimentsmay increase advertiser revenue, such as by enabling the advertiser tolaunch automated, “first to advertise” campaigns well in advance ofcompetitors. Systems according to some embodiments use a collection ofmethods including, but not limited to, sensors, filters, samplingtechniques, and predictive modeling engines, for example.

Some embodiments include a recognition that effectively or optimallyspending advertising dollars, from the advertiser executive perspective,can be a difficult and challenging problem. Furthermore, spendingadvertising dollars on an otherwise good campaign with good content cannone the less result in an ineffective or low ROI campaign if the timingis incorrect or too slow. Late to market timing can be an enormousproblem, particularly as more mobile devices, across the globe, are usedto create and transmit real-time data. Mobile users are creating andtransmitting, which can include broadcasting, data at a materiallyfaster rate than existing advertising platforms generally can intake,absorb, and process in a timely, relevant and optimal manner, from atargeting and campaign perspective. Some embodiments of the inventionreduce the time, such as to near-zero duration latency, such as bylaunching or enabling segmented and targeted campaigns as increasinglylarge scale and speed data streams, or “Big Data” streams, areoccurring. For example, some embodiments, enable, help enable, or launchextremely fast to market and optimized targeted advertising campaignsbased on real-time data, such as cell phone communications, socialnetworking blogs or feeds, etc.

Some embodiments include a recognition that obtaining relevant insightsusing existing systems and methods against the increasingly large BigData streams is no longer generally viable. Existing advertising systemsmay take too much time to load relevant Big Data, such as user andaudience data, analyze the data, give insights for advertisers to makedecisions, and, finally, to launch campaigns based on these thissequential or waterfall processing and time intensive processes. Thiscan result in too many or too slow steps, which take too much time tolaunch or help with a real-time, optimally targeted campaign.

Some embodiments provide new and real-time or near real-time approachesto detect, sample, and launch timely campaigns based upon real-time databeing created or transmitted. This can enable the advertiser the abilityto launch, or have launched on the advertiser's behalf, relevant andtargeted campaigns far sooner than competitors who use traditionalmethods to load and analyze data in a database for campaign launch,targeting and operationalization.

Some embodiments provide a real-time sampling, sensing, and detectionsystem capable of launching low latency advertising campaigns, allowingnear-instantaneous campaigns based on data just being created and orjust being transmitted.

Some embodiments provide techniques including a collection of methodsand subsystems using hardware, software, databases, and moderncommunication methods that integrate into existing data related systemssuch as cell phone transmission and reception structures (broadlyincluding towers, repeaters, etc.), clouds and cloud computing systems,databases, or applications, for example. Furthermore, some embodimentsinclude use of real-time systems and methods, such as real-time filters,sensors, digital processing, and analytics, to name a few methods, tosample for relevant, real-time, suspect target data, which suspect datacan include data that is suspected of or identified as being ofparticular significance or relevance in determining topics of interestto electronic device users. Still further, some embodiments also usereal-time systems and methods, such as real-time filters, sensors,digital processing, and analytics, to name a few methods, to ignore thebulk of real-time data. Additionally, some embodiments use real-timesystems and methods, such as real-time filters, sensors, digitalprocessing, and analytics, for example, to filter, amplify, correlateand, examine suspect data and identify or generate desirable targetingdata, which may be, for example, relevant or particularly relevant to apublisher or advertiser, for example. Some embodiments further includetriggering or activation, or messaging relating to or leading totriggering or activation, of real-time advertising campaigns.

Some embodiments include components, systems or devices that can beplaced in any of various physical or conceptual places in the datacreation, data intermediary and overall transmission chain. In someembodiments, subsystems can be placed in multiple distributed locations,and a central controlling system can be used to monitor all subsystems,to perform massive and scalable data detection, campaign launching, etc.This can improve data detection accuracy and improve global campaigntargeting, for example.

Some embodiments include systems that can identify real-time gaps inglobal communication, known as imperfect information distributions, suchas by monitoring data streams and stream activity levels, and exploitthese imperfect distribution gaps to a publisher or advertiser'sadvantage, for example.

Big Data creation and transmission, which can occur prior to Big Databeing traditionally captured and stored, is becoming increasingly largeand more complex as more data sources are creating and transmittingdata. Traditional processes, which may involve capturing Big Data andthen running advertising campaigns and or running analytics against BigData to provide segmented and targeted campaigns, have already reached asunset stage in terms of capability and maturity.

Some embodiments provide systems and methods that enable advertisers toincrease topline revenue by creating campaigns fast enough toaccommodate the speed of user data creation and transmission, as opposedto steps including waiting for the data to be received by a publisher,then performance of analytics in order to gain insights, and then therunning of campaigns. With traditional techniques, the first-moveropportunity and revenue, in terms of time, can be lost.

Some embodiments include use of sophisticated digital and analogtechniques, applied to real-time data processing well into the earlydata creation and transmission stream and data life cycle, givingadvertisers and publishers unprecedented marketing speed advantages.

One Chief Marketing Officer, or CMO, ideal goal is to create the perfectadvertising campaign by defining an ideal customer profile andsurgically targeting only audiences that match ideal customers. Thisperfect, albeit a utopia, state of alignment, increases advertiser'srevenue, increases advertising campaign ROI, and reduces ad campaignexpenses on campaigns targeting the incorrect audiences as the alignmentgap decreases. Providing the right ad campaign at the earliest possibleand most relevant time can help the CMO increase revenue, includingproviding the right ad at the right time, such as real-time or nearreal-time.

Some embodiments enable advertisers to take immediate campaign action(or take campaign action without advertiser action) as data is beingcreated and or transmitted.

Some embodiments include allowing publishers and content providers tosell premium “first to act” service levels, increasing revenue andallowing positive differentiation from competitors.

Some embodiments reduce the time it takes for publishers and contentproviders to gain insights and consequently provide premium informationservices which command premium incremental revenue streams, in a senseproviding high-speed intelligence.

Techniques according to embodiments of the invention can be implementedutilized various topologies, to globally scale into an integrated numberof installations such as cell towers, publishers and content providerservers, clouds, data centers, and others.

While the invention is described with reference to the above drawings,the drawings are intended to be illustrative, and the inventioncontemplates other embodiments within the spirit of the invention.

1-20. (canceled)
 21. A computerized method comprising: sampling one ormore streams of electronic data, the electronic data comprising usercommunications data, to obtain sample data, wherein the samplingcomprises sensing and detecting user communications data comprisinguser-generated content data streams that are in transmission to, but notyet received by, intended recipients; analyzing the user-generatedcontent data streams in the sample data, wherein the analyzing includesidentifying suspect data; based on the analyzing of the user-generatedcontent data streams in the sample data, modifying the sampling duringat least one period based on a determination that the suspect data ismore likely to be concentrated during the at least one period thanduring other periods, wherein the modifying of the sampling isdetermined by utilizing one or more analytic correlation applications indetecting patterns, and wherein the patterns include one or moretime-based or frequency-based patterns associated with theuser-generated content data streams; based on the suspect data obtained,selecting electronic advertisements for serving to targeted electronicdevice users; and serving the selected electronic advertisements to thetargeted electronic device users.
 22. The method of claim 21, whereinmultiple sampling techniques are applied to one or more data streams.23. The method of claim 22, wherein the multiple sampling techniques areperformed serially.
 24. The method of claim 22, wherein the multiplesampling techniques are performed in a compound manner.
 25. The methodof claim 22, wherein the multiple sampling techniques are performedconcurrently.
 26. The method of claim 21, wherein the modifying is basedon one or more feedback mechanism predictive models or machine learningmodels.
 27. The method of claim 21, wherein the modifying includesincreasing one or more sampling frequencies or rates.
 28. The method ofclaim 21, wherein the one or more streams of electronic data are fromone geographic location or from multiple geographically distributedlocations.
 29. The method of claim 21, wherein the sampling comprisesmonitoring, at one or more locations, the one or more streams ofelectronic data during transmission.
 30. The method of claim 29, whereinthe sample data comprises one or more streams of electronic data at oneor more cell phone transmission or reception nodes.
 31. The method ofclaim 29, wherein the sample data is obtained from one or morerepeaters, computing systems, satellites, transmission systems, orreceiving systems, and wherein the sample data may include one or moreof video data, audio data, voice data, gaming data, social networkingdata, or blog data.
 32. The method of claim 29, wherein the sample datacomprises one or more streams of electronic data from a cloud computingdatabase or cloud computing data center.
 33. The method of claim 21,further comprising: sampling a plurality of streams of electronic data;and integrating data from the plurality of streams.
 34. The method ofclaim 21, wherein the sampling comprises intercepting sample datarelated to a plurality of data modes, wherein the data modes maycomprise any of voice, audio, video, gaming, social network or blogging.35. The method of claim 34, further comprising sampling the one or morestreams of electronic data at one or more intervals, wherein the one ormore intervals may be determined based on one or more parameters or oneor more algorithms, and wherein the one or more parameters or one ormore algorithms may include or incorporate any of sampling time,sampling frequency, or sampling data analytics.
 36. The method of claim21, wherein the identifying of the suspect data includes determining thesuspect data to be of significance or relevance in determining topics ofinterest to electronic device users.
 37. The method of claim 21, whereinthe sampling takes place at a same location as a data source or on asame data source system as a data source.
 38. A system comprising: oneor more server computers coupled to a network; and one or more databasescoupled to the one or more server computers; wherein the one or moreserver computers are configured to: sample one or more streams ofelectronic data, the electronic data comprising user communicationsdata, to obtain sample data, wherein the sampling comprises sensing anddetecting user communications data comprising user-generated contentdata streams that are in transmission to, but not yet received by,intended recipients; analyze the user-generated content data streams inthe sample data, wherein the analyzing includes identifying suspectdata; based on the analyzing of the user-generated content data streamsin the sample data, modify the sampling during at least one period basedon a determination that the suspect data is more likely to beconcentrated during the at least one period than during other periods,wherein the modifying of the sampling is determined by utilizing one ormore analytic correlation applications in detecting patterns, andwherein the patterns include one or more time-based or frequency-basedpatterns associated with the user-generated content data streams; basedon the suspect data obtained, select electronic advertisements forserving to targeted electronic device users; and serve the selectedelectronic advertisements to the targeted electronic device users. 39.The system of claim 38, further comprising sampling the one or morestreams of electronic data via one or more devices installed at one ormore cell phone transmission or reception systems.
 40. The system ofclaim 38, further comprising sampling the one or more streams ofelectronic data from a cloud computing system or cloud, and wherein thestreams of data can include any of voice data, video data, voice data,audio data, gaming data, social network data, or blog data.
 41. Anon-transitory computer readable storage medium or media having storedinstructions thereon for causing a computer to execute a method, themethod comprising: sampling one or more streams of electronic data, theelectronic data comprising user communications data, to obtain sampledata, wherein the sampling comprises sensing and detecting usercommunications data comprising user-generated content data streams thatare in transmission to, but not yet received by, intended recipients;analyzing the user-generated content data streams in the sample data,wherein the analyzing includes identifying suspect data; based on theanalyzing of the user-generated content data streams in the sample data,modifying the sampling during at least one period based on adetermination that the suspect data is more likely to be concentratedduring the at least one period than during other periods, wherein themodifying of the sampling is determined by utilizing one or moreanalytic correlation applications in detecting patterns, and wherein thepatterns include one or more time-based or frequency-based patternsassociated with the user-generated content data streams; based on thesuspect data obtained, selecting electronic advertisements for servingto targeted electronic device users; and serving the selected electronicadvertisements to the targeted electronic device users, the servingincluding causing an advertisement serving system to initiate or modifyan automated advertising campaign that includes the selected electronicadvertisements.